Multiagent systems have become popular over the last few years for building complex, adaptive systems in a distributed, heterogeneous setting. Multiagent systems tend to be more robust and, in many cases, more efficient than single monolithic applications. However, unpredictable application environments make multiagent systems susceptible to individual failures that can significantly reduce its ability to accomplish its overall goal. The problem is that multiagent systems are typically designed to work within a limited set of configurations. Even when the system possesses the resources and computational power to accomplish its goal, it may be constrained by its own structure and knowledge of its member's capabilities. To overcome these problems, we are developing a framework that allows the system to design its own organization at runtime. This paper presents a key component of that framework, a metamodel for multiagent organizations named the Organization Model for Adaptive Computational Systems. This model defines the requisite knowledge of a system's organizational structure and capabilities that will allow it to reorganize at runtime and enable it to achieve its goals effectively in the face of a changing environment and its agent's capabilities.Keywords: adaptation, organizations, metamodel, self-organization IntroductionSystems are becoming more complex, in part due to increased customer requirements and the expectation that applications should be seamlessly integrated with other existing, often distributed applications and systems. In addition, there is an increasing demand for these complex systems to exhibit some type of intelligence as well. No longer is it "good enough" to be able to access systems across the internet, but customers require that their systems know how to access data and systems, even in the face of unexpected events or failures.The goal of our research is to develop a framework for constructing complex, distributed systems that can autonomously adapt to their environment. Multiagent systems have become popular over the last few years for providing the basic notions that are applicable to this problem. A multiagent Scott A. DeLoach, Walamitien Oyenan & Eric T. Matson. A Capabilities Based Model for Artificial Organizations. Journal of Autonomous Agents and Multiagent Systems. Volume 16, no. 1, February 2008, pp. 13-56. DOI: 10.1007 (note: this text is identifiable to the journal, however, the format is notThe original publication is available at www. springerlink.com.) system uses groups of self-directed agents working together to achieve a common goal. Such multiagent systems are widely proposed as replacements for sophisticated, complex, and expensive stand-alone systems for similar applications. Multiagent systems tend to be more robust and, in many cases, more efficient (due to their ability to perform parallel actions) than single monolithic applications. In addition, the individual agents tend to be simpler to build, as they are built from a single agent's perspective...

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Abstract

An autonomic system is a system capable of managing itself and adjusting its actions in the face of environmental changes. Autonomic systems are currently developed using ad-hoc approaches, which do not promote repeatable successes. In this paper, we propose a systematic approach for designing autonomic systems. Our approach adopts a multiagent perspective based on the Organization Model for Adaptive Computational Systems, which defines the knowledge required for the system to be able to self-organize. Furthermore, a customized development process based on the Organization-based Multiagent Systems Engineering framework supports our approach. To illustrate the process, we describe the design of one autonomic system, the Autonomic Information System, and exemplify how this system fulfills desired autonomic properties. We also evaluate the performance of our autonomic system by comparing it to a non-autonomic system.

Organization-based Multiagent Systems are a promising way to develop complex multiagent systems. However, it is still difficult to create large multiagent organizations from scratch. Multiagent organizations created using current AOSE methodologies tend to produce ad-hoc designs that work well for small applications but are not easily reused. In this paper, we provide a conceptual framework for designing reusable multiagent organizations. It allows us to simplify multiagent organization designs and facilitate their reuse. We formalize the concepts required to design reusable organization-based multiagent services and show how we can compose those services to create larger, more complex multiagent systems. We demonstrate the validity of our approach by designing an application from the cooperative robotics field.